Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data
Predicting residential indoor concentrations of nitrogen dioxide, fine particulate matter, and elemental carbon using questionnaire and geographic information system based data
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DOI：
10.1016/j.atmosenv.2007.04.027
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发表时间：
2007-10-01
影响因子：
5
通讯作者：
Levy, Jonathan I.
中科院分区：
文献类型：
--
作者：
Baxter, Lisa K.;Clougherty, Jane E.;Levy, Jonathan I.
以前的研究已经确定了与交通相关的空气污染与有害健康影响之间的联系。大多数人使用几个中央环境监测仪的测量结果和/或交通流量的一些测量作为暴露的指标，而忽略了空间变异性和影响个人暴露-环境浓度关系的因素。本研究旨在利用可公开获得的数据(即中央站点监测仪、地理信息系统和财产评估数据)和问卷回复来预测较低社会经济地位(SES)城市家庭居住室内与交通相关的空气污染物浓度。作为波士顿市区一项前瞻性出生队列研究的一部分，我们收集了2003-2005年多个季节43个SES较低家庭的室内和室外3-4天的二氧化氮(NO2)和细颗粒物(PM2.5)样本。元素碳(EC)浓度通过反射光谱分析测定。使用马萨诸塞州公路局的数据和在采样房屋外收集的交通计数得出了多个交通指标。通过标准化问卷收集家庭特征和居住者行为。通过财产税记录收集额外的住房信息，从中央位置的环境监测仪收集环境浓度，通过回归分析量化环境浓度、当地交通和室内污染源对室内浓度的贡献。PM2.5受当地交通影响较小，但具有显著的室内来源，而EC与交通和NO2有关，交通和室内来源都与PM2.5有关。使用p值或贝叶斯方法比较基于协变量选择的模型得到了类似的结果，家庭50米缓冲区内的交通密度和离卡车路线的距离分别是影响室内NO2和EC水平的重要因素。贝叶斯方法也突显了模型中的不确定性。我们的结论是，通过利用公共数据库和有重点的问卷数据，我们可以确定高危人群中多种空气污染物室内浓度的重要预测因素。(C)2007爱思唯尔有限公司。保留所有权利。
Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system, and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households.As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3-4 day samples of nitrogen dioxide (NO2) and fine particulate matter (PM2.5) in 43 low SES residences across multiple seasons from 2003 to 2005. Elemental carbon (EC) concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally located ambient monitor.The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO2 with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50 m buffer of a home and distance from a truck route as important contributors to indoor levels of NO2 and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and focused questionnaire data we can identify important predictors of indoor concentrations for multiple air pollutants in a high-risk population. (c) 2007 Elsevier Ltd. All rights reserved.